


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow







class 							lowerCamelCase__						:






			"""simple docstring"""






			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						return None







class 							lowerCamelCase__						:






			"""simple docstring"""






			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						return None







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       str 			=		[
			    # (model_name, model_kwargs)
			    ('bert-base-cased', {}),
			    ('gpt2', {'use_cache': False}),  # We don't support exporting GPT2 past keys anymore
			]




			@require_tf
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									self._test_export(snake_case   ,    "tf"   ,    12   ,    **snake_case  )




			@require_torch
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									self._test_export(snake_case   ,    "pt"   ,    12   ,    **snake_case  )




			@require_torch
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						from transformers import BertModel

						UpperCamelCase__       =							["[UNK]", "[SEP]", "[CLS]", "[PAD]", "[MASK]", "some", "other", "words"]
						with NamedTemporaryFile(mode="w+t"  ) as vocab_file:
									vocab_file.write("\n".join(snake_case  )  )
									vocab_file.flush()
									UpperCamelCase__       =							BertTokenizerFast(vocab_file.name  )

						with TemporaryDirectory() as bert_save_dir:
									UpperCamelCase__       =							BertModel(BertConfig(vocab_size=len(snake_case  )  )  )
									model.save_pretrained(snake_case  )
									self._test_export(snake_case   ,    "pt"   ,    12   ,    snake_case  )




			@require_tf
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									UpperCamelCase__       =							self._test_export(snake_case   ,    "tf"   ,    12   ,    **snake_case  )
									UpperCamelCase__       =							quantize(Path(snake_case  )  )

									# Ensure the actual quantized model is not bigger than the original one
									if quantized_path.stat().st_size >= Path(snake_case  ).stat().st_size:
												self.fail("Quantized model is bigger than initial ONNX model"  )




			@require_torch
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
									UpperCamelCase__       =							self._test_export(snake_case   ,    "pt"   ,    12   ,    **snake_case  )
									UpperCamelCase__       =							quantize(snake_case  )

									# Ensure the actual quantized model is not bigger than the original one
									if quantized_path.stat().st_size >= Path(snake_case  ).stat().st_size:
												self.fail("Quantized model is bigger than initial ONNX model"  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case=None   ,    **snake_case  ):

						'''simple docstring'''


						try:
									# Compute path
									with TemporaryDirectory() as tempdir:
												UpperCamelCase__       =							Path(snake_case  ).joinpath("model.onnx"  )

									# Remove folder if exists
									if path.parent.exists():
												path.parent.rmdir()

									# Export
									convert(snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    snake_case   ,    **snake_case  )

									return path
						except Exception as e:
									self.fail(snake_case  )




			@require_torch
			@require_tokenizers
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						from transformers import BertModel

						UpperCamelCase__       =							BertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"  )  )
						UpperCamelCase__       =							BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"  )
						self._test_infer_dynamic_axis(snake_case   ,    snake_case   ,    "pt"  )




			@require_tf
			@require_tokenizers
			@slow
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						from transformers import TFBertModel

						UpperCamelCase__       =							TFBertModel(BertConfig.from_pretrained("lysandre/tiny-bert-random"  )  )
						UpperCamelCase__       =							BertTokenizerFast.from_pretrained("lysandre/tiny-bert-random"  )
						self._test_infer_dynamic_axis(snake_case   ,    snake_case   ,    "tf"  )




			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							FeatureExtractionPipeline(snake_case   ,    snake_case  )

						UpperCamelCase__       =							["input_ids", "token_type_ids", "attention_mask", "output_0", "output_1"]
						UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__,   UpperCamelCase__       =							infer_shapes(snake_case   ,    snake_case  )

						# Assert all variables are present
						self.assertEqual(len(snake_case  )   ,    len(snake_case  )  )
						self.assertTrue(all(var_name in shapes for var_name in variable_names  )  )
						self.assertSequenceEqual(variable_names[:3]   ,    snake_case  )
						self.assertSequenceEqual(variable_names[3:]   ,    snake_case  )

						# Assert inputs are {0: batch, 1: sequence}
						for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
									self.assertDictEqual(shapes[var_name]   ,    {0: "batch", 1: "sequence"}  )

						# Assert outputs are {0: batch, 1: sequence} and {0: batch}
						self.assertDictEqual(shapes["output_0"]   ,    {0: "batch", 1: "sequence"}  )
						self.assertDictEqual(shapes["output_1"]   ,    {0: "batch"}  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							["input_ids", "attention_mask", "token_type_ids"]
						UpperCamelCase__       =							{"input_ids": [1, 2, 3, 4], "attention_mask": [0, 0, 0, 0], "token_type_ids": [1, 1, 1, 1]}
						UpperCamelCase__,   UpperCamelCase__       =							ensure_valid_input(FuncContiguousArgs()   ,    snake_case   ,    snake_case  )

						# Should have exactly the same number of args (all are valid)
						self.assertEqual(len(snake_case  )   ,    3  )

						# Should have exactly the same input names
						self.assertEqual(set(snake_case  )   ,    set(snake_case  )  )

						# Parameter should be reordered according to their respective place in the function:
						# (input_ids, token_type_ids, attention_mask)
						self.assertEqual(snake_case   ,    (tokens["input_ids"], tokens["token_type_ids"], tokens["attention_mask"])  )

						# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
						UpperCamelCase__,   UpperCamelCase__       =							ensure_valid_input(FuncNonContiguousArgs()   ,    snake_case   ,    snake_case  )

						# Should have exactly the one arg (all before the one not provided "some_other_args")
						self.assertEqual(len(snake_case  )   ,    1  )
						self.assertEqual(len(snake_case  )   ,    1  )

						# Should have only "input_ids"
						self.assertEqual(inputs_args[0]   ,    tokens["input_ids"]  )
						self.assertEqual(ordered_input_names[0]   ,    "input_ids"  )







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							generate_identified_filename(Path("/home/something/my_fake_model.onnx"  )   ,    "-test"  )
						self.assertEqual("/home/something/my_fake_model-test.onnx"   ,    generated.as_posix()  )






import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard







@slow
@require_flax
class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						super().tearDown()
						gc.collect()




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-canny"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"bird"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([canny_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2







			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__,   UpperCamelCase__       =							FlaxControlNetModel.from_pretrained(
						    "lllyasviel/sd-controlnet-openpose"   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__,   UpperCamelCase__       =							FlaxStableDiffusionControlNetPipeline.from_pretrained(
						    "runwayml/stable-diffusion-v1-5"   ,    controlnet=snake_case   ,    from_pt=snake_case   ,    dtype=jnp.bfloataa  )
						UpperCamelCase__       =							controlnet_params

						UpperCamelCase__       =							"Chef in the kitchen"
						UpperCamelCase__       =							jax.device_count()
						UpperCamelCase__       =							pipe.prepare_text_inputs([prompts] * num_samples  )

						UpperCamelCase__       =							load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"  )
						UpperCamelCase__       =							pipe.prepare_image_inputs([pose_image] * num_samples  )

						UpperCamelCase__       =							jax.random.PRNGKey(0  )
						UpperCamelCase__       =							jax.random.split(snake_case   ,    jax.device_count()  )

						UpperCamelCase__       =							replicate(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )
						UpperCamelCase__       =							shard(snake_case  )

						UpperCamelCase__       =							pipe(
						    prompt_ids=snake_case   ,    image=snake_case   ,    params=snake_case   ,    prng_seed=snake_case   ,    num_inference_steps=50   ,    jit=snake_case   ,    ).images
						assert images.shape == (jax.device_count(), 1, 768, 512, 3)

						UpperCamelCase__       =							images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
						UpperCamelCase__       =							images[0, 253:256, 253:256, -1]

						UpperCamelCase__       =							jnp.asarray(jax.device_get(image_slice.flatten()  )  )
						UpperCamelCase__       =							jnp.array(
						    [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]  )
						print(F'''output_slice: {output_slice}'''  )
						assert jnp.abs(output_slice - expected_slice  ).max() < 1E-2


